RL Benchmarks

RL: Reinforcement Learning

Reinforcement learning benchmarks test how agents learn and act across settings such as continuous control, games, robotics, healthcare, energy, and multi-agent coordination. They range from GPU-accelerated simulators and procedurally varied tasks to offline datasets and physical platforms, often pairing standard algorithms with calibrated baselines. Evaluations increasingly examine more than reward, including safety violations, generalization to shifted environments, robustness, sample efficiency, and training cost. A central challenge is balancing realistic, diverse tasks with reproducible comparisons: algorithm rankings can change with task design, action rules, reward choices, and deployment conditions.

Latest papers

85 papers15 papers in the last four weeks, up 114% on the four weeks before. 0.1% of all new papers.

RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms
Oct 23, 2026Xi Chen, Fengqi Hu, Zeyu Shen +5Robust RL RL Benchmarks
Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. · Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA. · University of California San Diego, La Jolla, CA 92093, USA. +3
Ground-Truth-First Benchmarking of Reinforcement Learning via Converse Optimality: From Smooth, Fully Observed Systems to Non-Smooth or Partially Observed Tasks
Oct 22, 2026Grégoire Ouerdane, Sinan Ibrahim, Stefan Streif +1RL Benchmarks Robust RL
Center for Engineering Systems and Sciences, Skoltech, 30 Bolshoy Boulevard, bld. 1, Moscow, 121205, Russia · Central University, 7 Gasheka St., bld. 1, Moscow, 123056, Russia · Technische Universität Chemnitz, Straße der Nationen 62, D-09111 Chemnitz, Germany +1
JAXtari: High-Throughput and Easy-to-Modify Arcade Learning Environment
Oct 16, 2026Quentin Delfosse, Raban Emunds, Paul Seitz +5GPU-Accelerated RL RL Benchmarks
Department of Computer Science, Technical University Darmstadt, Germany · Google Intrinsic AI Research · Hessian Center for Artificial Intelligence (hessian.AI) +2
More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning
Oct 14, 2026Linhao Zhong, Bozhen Fang, Xuehao Wang +7Agentic RL Self-Distillation
Zhejiang University, State Lab of CAD & CG · Ant Group
MARL-BattleGrounds: A Novel JAX-native Benchmark for Heterogeneous and Competitive Multi-agent RL
Oct 10, 2026Ulixes Tariq Hawili, Subramanian Ramamoorthy, Yoann Altmann +1RL Benchmarks Multi-Agent Reinforcement Learning
School of Engineering, University of Edinburgh, Edinburgh, United Kingdom · School of Informatics, University of Edinburgh, Edinburgh, United Kingdom · School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom
BluffJAX: Adversarial Imperfect Information Games in JAX
Oct 6, 2026Aryaman Reddi, Jan Peters, Carlo D'EramoImperfect-Information Games RL Benchmarks
Department of Computer Science, TU Darmstadt, Germany · Hessian Center for Artificial Intelligence (Hessian.ai), Germany · German Research Center for AI (DFKI), Systems AI for Robot Learning, Germany +1
Factoriax: A GPU-Accelerated Factorio-Style Simulator for Reinforcement Learning
Oct 4, 2026Mickey Beurskens, Tristan Tomilin, Thiago D. SimãoRL Benchmarks Reinforcement Learning
Eindhoven University of Technology
A Bird's-Eye View of Iterative Reward Design
Oct 3, 2026Logan Mondal Bhamidipaty, Lauren Robson, Linda Petrini +2Reward Design for RL LLM-Guided RL
Anthropic Fellows Program · University of Edinburgh · Anthropic
Co-design Gym: A Unified Benchmark for Embodiment-Policy Co-optimization
Oct 1, 2026Aviraj Newatia, Yordan Tsvetkov, Leonard Pleiss +2Robot Morphology-Control Co-Design RL Benchmarks
University of Cambridge · Technical University of Munich · Carnegie Mellon University +1
ALER: Adaptive Learnable Experience Rewriting for Reinforcement Learning
Sep 30, 2026Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov +1RL Benchmarks Reinforcement Learning
Innopolis University, Innopolis, Russia · MIRIAI, Moscow, Russia · Cognitive AI Systems Lab, Moscow, Russia
CATCH: A Controllable Analysis Testbed for Reward Hacking in Coding RL
Sep 30, 2026Shouli Wang, Yanfeng Jia, Zhihao Ou +6RL Benchmarks RL for Code Generation
Tsinghua University · Beihang University · Southern University of Science and Technology +2
Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL
Sep 29, 2026Mathias Jackermeier, Jacques Cloete, Alessandro AbateRL Benchmarks Reinforcement Learning
Department of Computer Science, University of Oxford · Oxford Robotics Institute, University of Oxford
PowerMarketJax: A JAX Benchmark Suite for Multi-Agent Reinforcement Learning in Power Markets
Sep 29, 2026Zhanhua Pan, Xin Qin, Xiao Liu +3RL Benchmarks Multi-Agent Reinforcement Learning
Nanyang Technological University, Singapore. · Cornell University, USA. · University of Bristol, UK.
PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learning
Sep 28, 2026Zhanhua Pan, Xiao Liu, Zhilong Cao +2RL Benchmarks Constrained RL
Nanyang Technological University, Singapore. · Cornell University, USA. · University of Bristol, UK.
GlyphBench: A Playground for Language-Model Reinforcement Learning
Sep 28, 2026Roger Creus Castanyer, Marc-Alexandre Côté, Matthew James Sargent +3RL Benchmarks RL for Language Models
Mila - Quebec Artificial Intelligence Institute · Université de Montréal · Vmax
What Does a ProcGen Generalization Gap Measure? Action Rules, Residual Entropy, and the Missing Random Floor
Sep 26, 2026Abhisek KeshariReinforcement Learning Generalization in RL
Independent researcher
Deep Reinforcement Learning for Equity Trading: Benchmarking Actor-Critic Methods with Forward Retraining
Sep 25, 2026Bicheng Wang, Xinyi ZhangAlgorithmic Trading Reinforcement Learning
Stanford University
ARCGym: Benchmarking Deep Reinforcement Learning in Autonomous Robotic Colonoscopy
Sep 19, 2026Guanglin Ji, Martina Finocchiaro, Kenny Erleben +1Robotic Endoscopy RL Benchmarks
Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
REVERSAL-BENCH: A Reversibility Axis and Reset Oracle for Measuring the Reset-Free RL Cliff
Sep 15, 2026Riyaaz Shaik, Chandru VenkataramanRL Benchmarks Reinforcement Learning
Apple
Evaluation Metrics for Safe Reinforcement Learning
Sep 14, 2026Lindsay Spoor, Aske Plaat, Thomas MoerlandRL Benchmarks Constrained RL
Leiden Institute of Advanced Computer Science, Leiden University, Leiden, the Netherlands
VRL-Bench: Benchmarking agents on computer control tasks under finite trial budgets
Sep 11, 2026Yu Bai, Yukai Miao, Dawei Wang +8RL Benchmarks Reinforcement Learning
Zhongguancun Laboratory · Tsinghua University · Network Management Center, China Mobile
Sim2Signal: Sim-to-Real Benchmarks for Traffic Signal Control
Sep 1, 2026Ferdous Al Rafi, Susrik Mukherjee, Latika Liladhar Dekate +6RL Benchmarks Sim-to-Real Transfer
Arizona State University
BVR Sim: An Open and High-Throughput Environment for Heterogeneous Air-Combat Reinforcement Learning
Aug 26, 2026Haocheng Sun, Mulai TanRL Benchmarks Reinforcement Learning
Beijing University of Posts and Telecommunications · Air Force Engineering University · Aviation Engineering School, Air Force Engineering University Xi’an 710038, China
ShuttleArena: Interpretable Self-Play in Physics-Based Badminton
Aug 26, 2026Peize DingGame-Playing Agents Self-Play RL
Columbia University New York, NY 10027, USA
Chronocooked: A Benchmark for Interval Timing in Reinforcement Learning Agents
Aug 17, 2026Amrapali Pednekar, Alvaro Garrido-Perez, Yara Khaluf +1RL Benchmarks
Department of Information Technology IDLab, Ghent University - imec · Computer Science Department Vrije Universiteit Amsterdam
DSLE: A Learning Environment for Dark Souls Boss Encounters
Aug 10, 2026Derin Gezgin, Jim O'Connor, Tanner Goodwin +1RL Benchmarks Game-Playing Agents
Autonomous Agent Learning Lab Connecticut College New London, Connecticut, USA
TOUR: A Trajectory-Level Unlearning Benchmark for Offline Reinforcement Learning
Jul 23, 2026Chaofan Pan, Lingfei Ren, Xiangyu Jiang +6RL Benchmarks Membership Inference Attacks
1Southwestern University of Finance and Economics · 2Shanxi University
Building2Building: A Large Scale Benchmark for Generalizable Real-World Reinforcement Learning
Jul 17, 2026Vincent Taboga, Justin Veilleux, Doseok Jang +2RL Benchmarks Reinforcement Learning
1Mila - Quebec AI Institute · 2Université de Montréal - Département d’Informatique et de Recherche Opérationnelle
OOD-RL-Bench: A Benchmark Framework for Out-of-Distribution Detection in Reinforcement Learning
Jul 14, 2026Emil Mittag, Richard Dazeley, Peter VamplewRL Benchmarks Online Anomaly Detection
Deakin University, Geelong, Victoria, 3220, Australia · Federation University Australia, Ballarat, Victoria, 3350, Australia
Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems
Jul 11, 2026Rongping Zhou, Omid Tavallaie, Shuaijun Chen +1RL Benchmarks Reinforcement Learning
School of Computer Science, The University of Sydney, Camperdown NSW 2050, Australia · Department of Engineering Science, University of Oxford, Wellington Square, Oxford OX1 2JD, United Kingdom · Department of Computer Science, The University of Western Australia, 241, 35 Stirling Hwy, Crawley WA 6009

Oct 23, 2026J/K move · Enter open · S save

RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms

Xi Chen, Fengqi Hu, Zeyu Shen, Jiaying Xu+4

Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA. · Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21218, USA. · University of California San Diego, La Jolla, CA 92093, USA.+3

Robust RL RL Benchmarks Robotic RL

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Robust reinforcement learning (RL) is critical for reliable real-world deployment, where policies may fail under diverse shifts and disruptions. Existing robust RL algorithms offer a rich set of mechanisms and principles, yet two major barriers limit their broader potential: limited algorithm reusability and an unclear empirical capability boundary. We introduce RobustRLlib, an algorithm-centric library and benchmark that addresses both challenges. It integrates 17 representative robust online, offline, and safe RL algorithms under a unified interface for substantially improved reusability, together with an enhanced evaluation platform spanning diverse real-world shifts and advanced humanoid and vision-language-action tasks. Systematic evaluation over 273 perturbation configurations provides an up-to-date capability map of current robust RL algorithms, revealing where they remain effective and where substantial robustness gaps persist. As an open-source, user-friendly platform, RobustRLlib offers a reproducible and extensible codebase for applying existing methods to new problems and developing new robust RL algorithms.